%0 Journal Article
%T Development and clinical validation of a deep learning-based knee CT image segmentation method for robotic-assisted total knee arthroplasty.
%A Liu X
%A Li S
%A Zou X
%A Chen X
%A Xu H
%A Yu Y
%A Gu Z
%A Liu D
%A Li R
%A Wu Y
%A Wang G
%A Liao H
%A Qian W
%A Zhang Y
%J Int J Med Robot
%V 20
%N 4
%D 2024 Aug
%M 38994900
%F 2.483
%R 10.1002/rcs.2664
%X <B>BACKGROUND: </B>This study aimed to develop a novel deep convolutional neural network called Dual-path Double Attention Transformer (DDA-Transformer) designed to achieve precise and fast knee joint CT image segmentation and to validate it in robotic-assisted total knee arthroplasty (TKA).<BR><B>METHODS: </B>The femoral, tibial, patellar, and fibular segmentation performance and speed were evaluated and the accuracy of component sizing, bone resection and alignment of the robotic-assisted TKA system constructed using this deep learning network was clinically validated.<BR><B>RESULTS: </B>Overall, DDA-Transformer outperformed six other networks in terms of the Dice coefficient, intersection over union, average surface distance, and Hausdorff distance. DDA-Transformer exhibited significantly faster segmentation speeds than nnUnet, TransUnet and 3D-Unet (p < 0.01). Furthermore, the robotic-assisted TKA system outperforms the manual group in surgical accuracy.<BR><B>CONCLUSIONS: </B>DDA-Transformer exhibited significantly improved accuracy and robustness in knee joint segmentation, and this convenient and stable knee joint CT image segmentation network significantly improved the accuracy of the TKA procedure.